OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
This work addresses a foundational problem for AI researchers by providing a unified codebase and definition, though it is incremental as it builds on existing world model concepts without introducing new methods.
The paper tackles the lack of a clear and unified definition for world models in AI by introducing OpenWorldLib, a standardized inference framework that categorizes essential capabilities and integrates models across tasks for efficient reuse and collaborative inference.
World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we introduce OpenWorldLib, a comprehensive and standardized inference framework for Advanced World Models. Drawing on the evolution of world models, we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world. We further systematically categorize the essential capabilities of world models. Based on this definition, OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference. Finally, we present additional reflections and analyses on potential future directions for world model research. Code link: https://github.com/OpenDCAI/OpenWorldLib